Jayendra S. Jadhav

@vupune.ac.in

Assistant Professor, Artificial Intelligence
Vishwkarma University

Jayendra S. Jadhav


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https://researchid.co/jayendrajadhav

EDUCATION

PHD (Appearing), M.Tech (IIT Bombay), B.E. (GCOEA)

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Engineering, Information Systems, Information Systems and Management
8

Scopus Publications

213

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • A Review Study on Personal Finance Manager Application in the Age of AI
    Nitish C. Gharde, Jayendra Jadhav, Vaibhav Gajananrao Ingole, Sonal Ajay Bankar, Sulbha Yadav
    2026 International Conference on Communication Computing and Emerging Technologies Ic3et 2026, 2026
    This paper presents a comprehensive review of Personal Finance Manager (PFM) applications with a particular focus on Artificial Intelligence driven solutions. As digital platforms increasingly mediate personal financial activities, intelligent systems are required to support budgeting, expense tracking and informed decision-making. A systematic literature review is conducted to analyse recent advancements in AI-enabled PFMs, clearly outlining the databases consulted, selection criteria and number of studies reviewed. Key technological trends, adoption challenges and research gaps are identified. In addition, a case study of an AI-powered Telegram-based personal finance assistant is presented, including its experimental design, dataset characteristics and baseline comparison with conventional tools. The study further discusses security, regulatory compliance and ethical considerations associated with deploying AI in personal finance applications. The findings demonstrate that conversational AI has significant potential to enhance user engagement and financial awareness while highlighting the need for responsible and transparent system design.
  • Automated Blood Cell Image Analysis for Cancer Detection
    Pavitha Nooji, Jayendra Jadhav, Sneha Thakkar, Laxmi Khengare, Shriyog More, Samruddhi Yadav
    Lecture Notes in Networks and Systems, 2026
  • A Unified and Interpretable Benchmarking Framework for Brain Tumor Classification Using Classical and Deep Learning Models
    Preeti Jain, Nitin Jain, Susheelkumar Panchikattil, Devidas Chikhale, Jayendra S Jadhav, Amol Sankpal
    International Journal of Drug Delivery Technology, 2026
    Accurate detection of brain tumors from magnetic resonance imaging (MRI) remains a clinically critical yet computationally challenging task due to high-dimensional image complexity and sensitivity to diagnostic errors. This study presents a unified hybrid diagnostic framework that systematically integrates classical machine learning algorithms and deep neural architectures within a standardized and reproducible benchmarking environment. Unlike model-centric investigations that evaluate isolated classifiers, the proposed framework establishes a controlled cross-paradigm experimental ecosystem in which regression-based, probabilistic, distance-driven, shallow neural, and convolutional models operate under identical preprocessing, validation, and testing protocols. Beyond comparative performance analysis, interpretability is incorporated through Gradient-weighted Class Activation Mapping (Grad-CAM), enabling visualization of spatial attention patterns underlying convolutional predictions. A multi-metric evaluation strategy including accuracy, precision, recall, and F1-score provides comprehensive assessment of diagnostic reliability. Experimental results demonstrate a consistent performance hierarchy, with convolutional neural networks achieving superior discriminative capability and improved tumor sensitivity relative to classical approaches. By combining standardized benchmarking, interpretability integration, statistical validation, and deployment-aware evaluation, the proposed framework contributes a reproducible methodological reference for evidence-guided algorithm selection in medical imaging. The study advances transparent and clinically aligned artificial intelligence for MRI-based brain tumor detection.
  • A Unified and Interpretable Benchmarking Framework for Brain Tumor Classification Using Classical and Deep Learning Models
    Preeti Jain, Nitin Jain, Susheelkumar Panchikattil, Devidas Chikhale, Jayendra S Jadhav, Amol Sankpal
    Signos Historicos, 2026
    Accurate detection of brain tumors from magnetic resonance imaging (MRI) remains a clinically critical yet computationally challenging task due to high-dimensional image complexity and sensitivity to diagnostic errors. This study presents a unified hybrid diagnostic framework that systematically integrates classical machine learning algorithms and deep neural architectures within a standardized and reproducible benchmarking environment. Unlike model-centric investigations that evaluate isolated classifiers, the proposed framework establishes a controlled cross-paradigm experimental ecosystem in which regression-based, probabilistic, distance-driven, shallow neural, and convolutional models operate under identical preprocessing, validation, and testing protocols. Beyond comparative performance analysis, interpretability is incorporated through Gradient-weighted Class Activation Mapping (Grad-CAM), enabling visualization of spatial attention patterns underlying convolutional predictions. A multi-metric evaluation strategy including accuracy, precision, recall, and F1-score provides comprehensive assessment of diagnostic reliability. Experimental results demonstrate a consistent performance hierarchy, with convolutional neural networks achieving superior discriminative capability and improved tumor sensitivity relative to classical approaches. By combining standardized benchmarking, interpretability integration, statistical validation, and deployment-aware evaluation, the proposed framework contributes a reproducible methodological reference for evidence-guided algorithm selection in medical imaging. The study advances transparent and clinically aligned artificial intelligence for MRI-based brain tumor detection.
  • WACSO: Wolf Crow Search Optimizer for Convolutional Neural Network Hyperparameter Optimization
    Rahul Rajendra Papalkar, Jayendra Jadhav, Tareek Pattewar, Vivek Thorat, Pallavi Morey, Mayur Deshmukh, Rajkumar Jagdale
    Neural Processing Letters, 2025
    Convolutional Neural Networks (CNNs) experience performance and training efficiency changes according to the selection of correct hyperparameters. The research presents WACSO which combines Crow Search Optimization with Grey Wolf Optimizer to improve Convolutional Neural Networks hyperparameter selection through a hybrid metaheuristic algorithm. The hybrid algorithm WACSO uses exploration parts from CSO together with GWO exploitation mechanics to obtain optimized performance. WACSO reaches higher classification accuracy than traditional optimization algorithms when performing tests on the MNIST and CIFAR-10 datasets along with Random Search and particle swarm optimization and genetic algorithms and standalone CSO and standalone GWO. The best classification results reached 98.9% accuracy levels on MNIST along with 91.5% accuracy levels on CIFAR-10. The final outcomes of this system depend on the combination of model structure along with dataset challenges and available computational power. The investigation demonstrates that mixing algorithms drawn from nature can lead to successful CNN hyperparameter optimization. The promising outcomes of WACSO depend on multiple variables including computation expenses and sensitive parameter adjustments and universal result adaptability between different datasets and network setups. Research into WACSO should expand to involve longer evaluations across multiple datasets and various models to confirm widespread usage.
  • Insights into Women’s Sentiments on Breast Cancer Detection, Causes, and Treatments: A Comprehensive Analysis
    Kavita Kumavat, Jayendra Jadhav, Trupti Shinde, Rahul Papalkar, Sulbha Yadhav, Sonal Bankar
    Artificial Intelligence in Oncology Cancer Diagnosis and Treatment Medical Imaging and Personalized Medicine, 2025
  • Translating Hybrid ANN-ARIMA Diagnostic Models for Early Detection of Oncological Biomarkers
    Rahul Rajendra Papalkar, Jayendra Jadhav, Harish Motekar, Pravin Nerkar, Snehal H. Kuche, Nikhil S. Band, Vinod M. Rathod
    Artificial Intelligence in Oncology Cancer Diagnosis and Treatment Medical Imaging and Personalized Medicine, 2025
  • A review study of the blockchain-based healthcare supply chain
    Jayendra S. Jadhav, Jyoti Deshmukh
    Social Sciences and Humanities Open, 2022
    Technological acclimatization in today's healthcare industry is a subject of new inventions. The worldwide Covid-19 epidemic has led to increase in the use of technology for healthcare supply chain, patient data management, and claims settlement. Data management in healthcare industry is a complex structure where multiple organizations provide proper supply chain services in day to day life. Improper data management disrupts the supply chain, which has a long-term impact on the healthcare sector. Various issues in the present supply chain must be addressed. Blockchain-based crypto-currencies are well-known nowadays for their ability to create safe and traceable solutions. With the growing use of crypto-currencies, it also governs new range of applications and opportunities, including healthcare applications. Blockchain-based solutions are effective in the health sector for secure data retrieval and storage, resulting in more effectual product creation and tracking. Such system can provide data provenance, promotes genuine healthcare sector demands, and ensures the immutability of multi-direction transactions. In this study, we contribute a thorough overview of the literature on how Blockchain technology is changing the way healthcare supply chains operate. We looked at 61 papers from 2019 to 2021 that highlighted various difficulties with the traditional healthcare supply chain. We scrutinized different barriers and opportunity of Blockchain-based healthcare supply chain at the end of the research.

RECENT SCHOLAR PUBLICATIONS

  • A Review Study on Personal Finance Manager Application in the Age of AI
    NC Gharde, J Jadhav, VG Ingole, SA Bankar, S Yadav
    2026 International Conference on Communication, Computing and Emerging … , 2026
    2026
  • AI Powered ESP32 Energy Management System
    JS Jadhav, V Nigade, P Chavan, S Pawar, A Mehare, A Whandhekar
    International Conference on Sustainable Innovation with Artificial … , 2026
    2026
  • Federated ensemble learning framework for symptom-based lung cancer detection
    JS Jadhav, V Thorat, VG Ingole, D Bhise, P Landge, S Ali
    Artificial Intelligence and Sustainable Innovation, 308-313 , 2026
    2026
  • A robust deep learning approach for detecting COVID-19 and pneumonia in chest X-ray scans
    JS Jadhav, RR Papalkar, SN More, AM Pawar, SA Shinde, RV Kadam
    Artificial Intelligence and Sustainable Innovation, 352-356 , 2026
    2026
  • Automated detection of disc degeneration in X-ray images using deep learning CNNs
    JS Jadhav, T Shinde, D Varma, V Pandhare, A Deshmukh, V Ladkat
    Artificial Intelligence and Sustainable Innovation, 347-351 , 2026
    2026
  • A Blockchain-Integrated Machine Learning Framework for Early Detection of Unknown Viral Diseases in Healthcare Supply Chains: Design, Simulation, and Evaluation
    J S Jadhav, J Deshmukh
    University of Bahrain , 2025
    2025
  • Automated Blood Cell Image Analysis
    P Nooji, J Jadhav, S Thakkar, L Khengare, S More, S Yadav
    Proceedings of International Conference on AI Systems and Sustainable … , 2025
    2025
  • Translating Hybrid ANN-ARIMA Diagnostic Models for Early Detection of Oncological Biomarkers
    RR Papalkar, J Jadhav, H Motekar, P Nerkar, SH Kuche, NS Band, ...
    Artificial Intelligence in Oncology: Cancer Diagnosis and Treatment, Medical … , 2025
    2025
  • Insights into Women’s Sentiments on Breast Cancer Detection, Causes, and Treatments: A Comprehensive Analysis
    K Kumavat, J Jadhav, T Shinde, R Papalkar, S Yadhav, S Bankar
    Artificial Intelligence in Oncology: Cancer Diagnosis and Treatment, Medical … , 2025
    2025
  • Optimizing key performance indicators in cloud computing: Scheduling techniques
    B Kanchalwar, R Papalkar, J Jadhav, P Bhagat, S Hiremath, SV Mahajan
    Intelligent Computing and Communication Techniques, 282-287 , 2025
    2025
  • Enhancing cloud coverage detection in remote sensing imagery through deep learning and advanced feature extraction
    J Jadhav, R Papalkar, M Pal, P Morey, V Thorat, P Bhagat
    Intelligent Computing and Communication Techniques, 425-431 , 2025
    2025
  • Automated Blood Cell Image Analysis for Cancer Detection
    P Nooji, J Jadhav, S Thakkar, L Khengare, S More, S Yadav
    International Conference on AI Systems and Sustainable Technologies, 391-406 , 2025
    2025
  • WACSO: Wolf crow search optimizer for convolutional neural network hyperparameter optimization
    RR Papalkar, J Jadhav, T Pattewar, V Thorat, P Morey, M Deshmukh, ...
    Neural Processing Letters 57 (2), 31 , 2025
    2025
    Citations: 18
  • Artificial Intelligence in Energy Management: A Comprehensive Literature Review on Methods, Applications, and Challenges
    J Jadhav, A Mehare, A Wandhekar, S Pawar, P Chavan, V Nigade
    management 7, 8 , 2025
    2025
    Citations: 4
  • Advancing machine learning in COVID-19 diagnostics: Symptom-based classification and ensemble techniques
    JS Jadhav, J Deshmukh
    South Eastern European Journal of Public Health 3044, 3061 , 2025
    2025
    Citations: 8
  • Neuro-guard: Reinforcing web security with convolutional neural networks against cross-site scripting attacks
    RR Papalkar, J Jadhav, V Thorat, P Morey, M Pal, S Ali
    Intelligent Computing and Communication Techniques, 762-769 , 2025
    2025
    Citations: 13
  • Forecasting the future of healthcare expenses: The role of machine learning in insurance cost estimation
    P Morey, M Pal, J Jadhav, R Papalkar
    Intelligent Computing and Communication Techniques, 682-688 , 2025
    2025
  • Defence-against ransomware: smart technique to detect and mitigate attacks
    R Papalkar, AS Alvi, J Jadhav, M Pal, P Morey, V Thorat
    Journal of Engineering, Management and Information Technology 3 (03), 153-162 , 2025
    2025
    Citations: 6
  • An algorithm to study the mechanisms for exploring ChatGPT's effectiveness
    M Pal, P Morey, J Jadhav, R Papalkar, R Agnihotri, V Thorat
    Artificial Intelligence and Information Technologies, 540-546 , 2024
    2024
    Citations: 2
  • Technical aspects of robust multi-frame super-resolution image reconstruction across diverse scenes
    P Morey, M Pal, J Jadhav, R Papalkar, S Dash, R Agnihotri, V Thorat, ...
    Artificial Intelligence and Information Technologies, 535-539 , 2024
    2024
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • A Review Study of the Blockchain-Based Healthcare Supply Chain
    J Jadhav, J Deshmukh
    https://doi.org/10.1016/j.ssaho.2022.100328 , 2022
    2022
    Citations: 108
  • Securing the internet of things: Investigating common attacks and defense strategies for a resilient ecosystem
    R Papalkar, AS Alvi, J Jadhav, R Agnihotri, S Ali, V Thorat
    Artificial Intelligence and Information Technologies, 516-523 , 2024
    2024
    Citations: 25
  • WACSO: Wolf crow search optimizer for convolutional neural network hyperparameter optimization
    RR Papalkar, J Jadhav, T Pattewar, V Thorat, P Morey, M Deshmukh, ...
    Neural Processing Letters 57 (2), 31 , 2025
    2025
    Citations: 18
  • Neuro-guard: Reinforcing web security with convolutional neural networks against cross-site scripting attacks
    RR Papalkar, J Jadhav, V Thorat, P Morey, M Pal, S Ali
    Intelligent Computing and Communication Techniques, 762-769 , 2025
    2025
    Citations: 13
  • Navigating the path: Deep neural networks for accurate pothole and road quality detection
    J Jadhav, R Papalkar, P Morey, S Dash, M Pal, R Agnihotri, V Thorat, ...
    Artificial Intelligence and Information Technologies, 508-515 , 2024
    2024
    Citations: 11
  • A review on leech therapy
    R Ahirrao, J Jadhav, S Pawar
    Pharma Sci Monit 8, 228-237 , 2017
    2017
    Citations: 10
  • Advancing machine learning in COVID-19 diagnostics: Symptom-based classification and ensemble techniques
    JS Jadhav, J Deshmukh
    South Eastern European Journal of Public Health 3044, 3061 , 2025
    2025
    Citations: 8
  • Defence-against ransomware: smart technique to detect and mitigate attacks
    R Papalkar, AS Alvi, J Jadhav, M Pal, P Morey, V Thorat
    Journal of Engineering, Management and Information Technology 3 (03), 153-162 , 2025
    2025
    Citations: 6
  • Synergizing machine learning and blockchain for pioneering early disease detection: A focused study on COVID-19 diagnosis
    J Jadhav, J Deshmukh
    Available at SSRN 4794594 , 2024
    2024
    Citations: 5
  • Artificial Intelligence in Energy Management: A Comprehensive Literature Review on Methods, Applications, and Challenges
    J Jadhav, A Mehare, A Wandhekar, S Pawar, P Chavan, V Nigade
    management 7, 8 , 2025
    2025
    Citations: 4
  • Technical aspects of robust multi-frame super-resolution image reconstruction across diverse scenes
    P Morey, M Pal, J Jadhav, R Papalkar, S Dash, R Agnihotri, V Thorat, ...
    Artificial Intelligence and Information Technologies, 535-539 , 2024
    2024
    Citations: 3
  • An algorithm to study the mechanisms for exploring ChatGPT's effectiveness
    M Pal, P Morey, J Jadhav, R Papalkar, R Agnihotri, V Thorat
    Artificial Intelligence and Information Technologies, 540-546 , 2024
    2024
    Citations: 2
  • A Review Study on Personal Finance Manager Application in the Age of AI
    NC Gharde, J Jadhav, VG Ingole, SA Bankar, S Yadav
    2026 International Conference on Communication, Computing and Emerging … , 2026
    2026
  • AI Powered ESP32 Energy Management System
    JS Jadhav, V Nigade, P Chavan, S Pawar, A Mehare, A Whandhekar
    International Conference on Sustainable Innovation with Artificial … , 2026
    2026
  • Federated ensemble learning framework for symptom-based lung cancer detection
    JS Jadhav, V Thorat, VG Ingole, D Bhise, P Landge, S Ali
    Artificial Intelligence and Sustainable Innovation, 308-313 , 2026
    2026
  • A robust deep learning approach for detecting COVID-19 and pneumonia in chest X-ray scans
    JS Jadhav, RR Papalkar, SN More, AM Pawar, SA Shinde, RV Kadam
    Artificial Intelligence and Sustainable Innovation, 352-356 , 2026
    2026
  • Automated detection of disc degeneration in X-ray images using deep learning CNNs
    JS Jadhav, T Shinde, D Varma, V Pandhare, A Deshmukh, V Ladkat
    Artificial Intelligence and Sustainable Innovation, 347-351 , 2026
    2026
  • A Blockchain-Integrated Machine Learning Framework for Early Detection of Unknown Viral Diseases in Healthcare Supply Chains: Design, Simulation, and Evaluation
    J S Jadhav, J Deshmukh
    University of Bahrain , 2025
    2025
  • Automated Blood Cell Image Analysis
    P Nooji, J Jadhav, S Thakkar, L Khengare, S More, S Yadav
    Proceedings of International Conference on AI Systems and Sustainable … , 2025
    2025
  • Translating Hybrid ANN-ARIMA Diagnostic Models for Early Detection of Oncological Biomarkers
    RR Papalkar, J Jadhav, H Motekar, P Nerkar, SH Kuche, NS Band, ...
    Artificial Intelligence in Oncology: Cancer Diagnosis and Treatment, Medical … , 2025
    2025